Objective: To provide information of the occupation counts in each year in a table, and in several graphs (top 10, bottom 10, all ordered by size, all ordered by label)

It is noted that there appears to be a large disparity in the labelling between 1850 and 1880 with 1910. This was largely due to different occ codes used by IPUMS between the two time periods. 

The graphs shown are counted by `label` and not `occstr`. `occstr` provides a more consistent coding across the three time periods, but was not done as there are as many as 60 000 unique `occstr` in 1910. Further data cleaning would have to be done if `occstr` is to be graphed in full.

It is recommended that the next steps to unify the coding of `label` or `occstr` throughout the three time periods so that they can be compared against one another.

1. Set Up

README

In general, the data sets are relatively large, and take up a large amount of memory after loading. It is recommended that after loading, that the data sets be subsetted or summarised, and unused data frames to be removed.

The code is shown in the next three tabs to show any changes that may have been made. In general, no columns or rows are removed. Filtering occured in the next step.


Loading 1850

library(dplyr)
library(ggplot2)
library(treemap)
library(extrafont)
library(stringr)
loadfonts()
library(knitr)
library(kableExtra)
library(tidyr)

# Loading
mn_1850 = readr::read_csv("../Data/census_1850_occ_mn.csv") %>% mutate(city = "Manhattan")
bk_1850 = readr::read_csv("../Data/census_1850_occ_bk.csv") %>% mutate(city = "Brooklyn")
OCC_1880 = readr::read_csv("../Data/OCC1880.csv")

# Combining Data
combined_1850 = rbind(mn_1850, bk_1850) %>% 
  select(age, sex, marst, race, labforce,
         occ, city, everything()) %>%
  left_join(OCC_1880, by = c("occ" = "code")) %>%
  mutate(race = factor(race,
                       levels = c(100,120,200,210,300),
                       labels = c("White", "Black (white)",
                                  "Black / African American",
                                  "Mulatto", "American Indian")),
         labforce = factor(labforce,
                           levels = c(0,1,2),
                           labels = c("N/A",
                                      "No, not in the labor force",
                                      "Yes, in the labor force")),
         sex = factor(sex,
                      levels = c(1,2),
                      labels = c("Male", "Female")),
         marst = factor(marst,
                        levels = c(1:6),
                        labels = c("Married, spouse present",
                                   "Married, spouse absent",
                                   "Separated",
                                   "Divorced",
                                   "Widowed",
                                   "Never married/single")))

# Theme setting for visualisations
theme_set(theme_minimal() + 
            theme(panel.grid.minor = element_blank(),
                  text = element_text(family = "Quicksand",
                                      colour = "black")))
col_sex = c("#e21737", "#0b648f")
col_miss = c("#86b817", "#e53238")

# Saving Memory
rm(OCC_1880)
rm(bk_1850)
rm(mn_1850)


Loading 1880

# Loading
mn_1880 = readr::read_csv("../Data/census_1880_occ_mn.csv") %>% mutate(city = "Manhattan")
bk_1880 = readr::read_csv("../Data/census_1880_occ_bk.csv") %>% mutate(city = "Brooklyn")
OCC_1880 = readr::read_csv("../Data/OCC1880.csv")

# Combining Data
combined_1880 = rbind(mn_1880, bk_1880) %>% 
  select(age, sex, race, labforce,
         occ, city, everything()) %>%
  left_join(OCC_1880, by = c("occ" = "code")) %>%
  mutate(age = ifelse(age == "Less than 1 year old",
                      0,
                      age),
         age = as.numeric(age))

rm(OCC_1880)
rm(bk_1880)
rm(mn_1880)


Loading 1910

# Loading
mn_1910 = readr::read_csv("../Data/census_1910_occ_mn.csv") %>% mutate(city = "Manhattan")
bk_1910 = readr::read_csv("../Data/census_1910_occ_bk.csv") %>% mutate(city = "Brooklyn")
OCC_1950 = readr::read_csv("../Data/OCC1950.csv")

# Combining Data
combined_1910 = rbind(mn_1910, bk_1910) %>%
  left_join(OCC_1950, by = c("occ1950" = "code")) %>%
  mutate(sex = factor(sex,
                      levels = c(1,2),
                      labels = c("Male", "Female")),
         labor_force = factor(labor_force,
                              levels = c(0,1,2),
                              labels = c("N/A", 
                                         "No, not in the labor force",
                                         "Yes, in the labor force")))

rm(OCC_1950)
rm(mn_1910)
rm(bk_1910)


2. Occupations in Each Year

README

The following the three tabs are organised, with one for each year. Within each tab, there are five sub-tabs, a scrollable table with the counts of each occupation, two graph of the top and bottom 10 occupations, and two graphs of all occupations by count size and label order.

It is noted that the graphs are all produced using label as the variable, not occstr.

1850

Top Jobs
combined_1850 %>%
  filter(labforce == "Yes, in the labor force") %>%
  count(label) %>%
  arrange(desc(n)) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "300px")
label n
Laborers (not specified) 24730
Clerks in stores 14087
Sailors 8513
Carpenters and joiners 8390
Tailors and tailoresses 8244
Boot and shoemakers 7616
Traders and dealers (not specified) 7572
Draymen, hackmen, teamsters, etc. 6689
Masons (brick and stone) 3841
Traders and dealers in groceries 3768
Painters and varnishers 3381
Ship carpenters, caulkers, riggers, and smiths 3339
Cabinet makers 3300
Blacksmiths 3184
Bakers 2807
Porters and laborers in stores warehouses 2450
Machinists 2395
Printers, lithographers, and stereotypers 2374
Butchers 2331
Employees of hotels and restaurants (not clerks) 2295
Marble and stone cutters 1883
Gold and silver workers and jewelers 1854
Hucksters and peddlers 1703
Hat and cap makers 1469
Coopers 1337
Physicians and surgeons 1315
Saloon keepers and bartenders 1303
Lawyers 1287
Employees of government (not clerks) 1258
Tinners and tinware makers 1200
Cigar makers 1190
Iron and steel works and shops operatives 1165
Engineers and firemen 1097
Bookkeepers and accountants in stores 1047
Traders and dealers in produce and provisions 1041
Boatmen and watermen 985
Wood turners, carvers, and woodenware makers 982
Soldiers, sailors, and Marines (Army Navy) 910
Bookbinders and finishers 900
Barbers and hairdressers 884
Manufacturers 884
Musicians (professional) and teachers music 856
Farmers and planters 835
Gardeners, nurserymen, and vine-growers 769
Traders and dealers in dry foods, fancy foods, and notions 731
Plumbers and gasfitters 727
Cotton-mill operatives 723
Bankers and brokers 695
Builders and contractors (not specified) 663
Agents (not specified) 651
Harness and saddle makers 638
Hotel keepers 631
Teachers and scientific persons 624
Carriage and wagon makers 621
Gun- and lock-smiths 618
Steam boiler makers 617
Domestic servants 609
Pianoforte makers and tuners 593
Traders and dealers in drugs and medicines 593
Milkmen and milkwomen 571
Brass founders and workers 569
Clergy 568
Others in manufacturing, mechanical, and mining industries 562
Blind, door and sash makers 555
Leather curriers, dressers, finishers, and tanners 552
Rope and cordage makers 539
Clock and watchmakers and repairers 529
Traders and dealers in liquors and wines 496
Employees in manufacturing estabs. (not specified) 493
Wheelwrights 490
Glass-works operatives 487
Artists and teachers of art 478
Upholsterers 456
Agricultural laborers 452
Sail and awning makers 447
Engravers 445
Plasterers 433
Sawyers 431
Confectioners 428
Tool and cutlery makers 373
Traders and dealers in clothing and men’s furnishing goods 368
Fishermen and oystermen 365
Traders and dealers in cigars and tobacco 340
Officials of government 324
Gentleman 305
Sugar makers and refiners 298
Boarding- and lodging-house keepers 290
Restaurant keepers 289
Copper workers 285
Salesmen and saleswomen 284
Brewers and maltsters 283
Traders and dealers in coal and wood 277
Gilders 274
Hostlers 261
Box factory operatives 245
Pilots 244
Fur workers 238
Traders and dealers in iron, tin, and copperware 234
Broom and brush makers 231
Boat makers 224
Traders and dealers in boots and shoes 222
Candle, soap, and tallow makers 204
Officials and employees of trade and transportation companies (not specified) 204
Trunk, valise, and carpet-bag makers 202
Weighers, gaugers, and measurers 201
Livery-stable keepers 199
Umbrella and parasol makers 199
Dentists 198
Stewards and stewardesses 190
Bleachers, dyers and scourers 189
Traders and dealers in books stationary 186
Traders and dealers in junk 181
Journalists 165
Basket makers 155
Distillers and rectifiers 152
Whitewashers 144
Traders and dealers in lumber 142
Watchmen (private) and detectives 139
Auctioneers 138
Millers 138
Pattern makers 138
Carpet makers 132
Actors 131
Architects 131
Paper mill operatives 130
Traders and dealers in crockery, china, glass, and stoneware 130
Mirror and picture frame makers 126
Stove, furnace, and grate makers 126
Chemical-works employees 119
Bottlers and mineral-water makers 113
Brokers (commercial) 113
Newspaper criers and carriers 110
Undertakers 108
Apprentices to trades 107
Employees of railroad companies 107
Traders and dealers in leather, hides, and skins 107
Others in trade and transportation 106
Quarrymen 102
Leather case and pocket-book makers 98
Traders and dealers in cabinet ware 96
Engineers (civil) 95
Collectors and claim agents 92
Wire makers and workers 92
Sexton 89
Publishers of books, maps, and newspapers 88
Officials and employees of companies (not clerks) 86
Roofers and slaters 86
Packers 84
Chemists, assayers, and metallurgists 82
Lumbermen and raftsmen 82
Paperhangers 82
Traders and dealers in real estate 81
Mill and factory operatives (not specified) 80
Nail makers 80
Officers of the Army and Navy 79
Scale and rule makers 76
Traders and dealers in ice 71
Pump makers 70
Clerks in government offices 68
Rag pickers 67
Britannia and japanned ware makers 64
Potters 64
Steamboat men and women 61
Button-factory operatives 60
Saw and planing mill operatives 60
Galloon, gimp, and tassel makers 59
Organ makers 58
Stock-drovers 58
Traders and dealers in livestock 56
Photographers 55
Traders and dealers in oils, paints, and turpentine 55
Clerks and bookkeepers in banks 54
Mechanics (not specified) 53
Others in professional and services 53
Employees of insurance companies (clerks) 52
Miners 51
Charcoal and lime burners 50
Florists 48
Traders and dealers in music and musical instruments 44
Brick and tile makers 42
Janitors 42
Officials of banks 41
Artificial-flower makers 40
Traders and dealers in hats, caps, and furs 38
Employees of banks (not clerks) 37
Authors, lecturers, and literary persons 34
Bone and ivory workers 34
Hair cleaners, dressers, and workers 34
Rubber factory operatives 32
Launderers and laundresses 31
Shippers and freighters 31
Traders and dealers in paper and paper stock 31
Clerks and copyists 30
Designers, draughtsmen, and inventors 30
Traders and dealers in marble, stone and slate 30
Traders and dealers in cotton and wool 29
Showmen and employees of shows 28
Officials and employees of telegraph companies 27
Officials of insurance companies 27
Billiard- and bowling saloon keepers and employees 25
Clerks and bookkeepers in offices 24
Gas-works employees 24
Milliners, dressmakers, and seamstresses 24
Officials, industry not specified 23
Woolen mill operatives 23
Veterinary surgeons 22
Hosiery and knitting mill operatives 21
Meat packers, curers, and picklers 21
Stock-raisers 21
Messengers 20
Flax dressers 17
Straw workers 17
Officials of manufacturing and mining companies 16
Traders and dealers in gold and silverware and jewelry 16
Employees in warehouses 15
Meat and fruit preserving establishment employees 15
File makers, cutters, and grinders 14
Glove makers 14
Nurses 14
Shingle and lath makers 14
Salt makers 13
Silk mill operatives 13
Starch makers 13
Bridge builders and contractors 11
Lace makers 11
Agricultural implement makers 10
Commercial travelers 9
Employees of charitable institutions 9
Lead and zinc works operatives 9
Officials of railroad companies 8
Print-works operatives 8
Clerks and bookkeepers in manufacturing estabs. 7
Shirt, cuff, and collar makers 7
Dairymen and dairywomen 6
Others in agriculture 6
Toll-gate and bridge keepers 6
Car makers 5
Screw makers 5
Officials and employees of street companies 4
Railroad builders and contractors 4
Tobacco factory operatives 4
Clerks and bookkeepers in companies 3
Clerks and bookkeepers in railroad offices 3
Corset makers 3
Oil mill and refinery operatives 3
Wood choppers 3
Blank 2
Traders and dealers in agricultural implements 2
Turpentine farmers and laborers 2
Bag makers 1
Clerks in hotels and restaurants 1
Hunters, trappers, guides, and scouts 1
Other non-occupational response 1
Thread makers 1
  • Laborers appear to be the most popular occupation, followed by Clerks in Stores and Sailors.


Graph of Top 10 Occupations
combined_1850 %>%
  filter(labforce == "Yes, in the labor force") %>%
  count(label) %>%
  top_n(10, wt = n) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#f9a541") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 25000, 5000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of the Top 10 Most Popular Occupations in 1850") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of Bottom 10 Occupations
combined_1850 %>%
  filter(labforce == "Yes, in the labor force") %>%
  count(label) %>%
  top_n(-10, wt = n) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#f9a541") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 3, 1),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of the Top 10 Least Popular Occupations in 1850") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of All Occupations by Count
combined_1850 %>%
  filter(labforce == "Yes, in the labor force") %>%
  count(label) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#f9a541") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 25000, 5000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of All Occupations by Count in 1850") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of All Occupations by Type
combined_1850 %>%
  filter(labforce == "Yes, in the labor force") %>%
  count(label) %>%
  
  ggplot(aes(y = n, x = label, n)) +
  geom_col(fill = "#f9a541") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 25000, 5000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of All Occupations by Type in 1850") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


1880

Top Jobs
combined_1880 %>%
  filter(labforce == "Yes, in the labor force" & 
           label != "Blank") %>%
  count(label) %>%
  arrange(desc(n)) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "300px")
label n
Domestic servants 66273
Laborers (not specified) 50757
Clerks in stores 38060
Milliners, dressmakers, and seamstresses 28317
Tailors and tailoresses 26399
Draymen, hackmen, teamsters, etc. 18628
Carpenters and joiners 15074
Launderers and laundresses 11590
Cigar makers 11440
Porters and laborers in stores warehouses 11390
Boot and shoemakers 11045
Painters and varnishers 10943
Printers, lithographers, and stereotypers 10403
Butchers 9273
Employees of hotels and restaurants (not clerks) 9242
Saloon keepers and bartenders 8483
Bookkeepers and accountants in stores 7938
Traders and dealers in groceries 7258
Salesmen and saleswomen 6966
Bakers 6873
Machinists 6831
Teachers and scientific persons 5987
Masons (brick and stone) 5767
Hucksters and peddlers 5755
Engineers and firemen 5727
Traders and dealers in produce and provisions 5259
Blacksmiths 5226
Traders and dealers (not specified) 5141
Sailors 4710
Cabinet makers 4609
Barbers and hairdressers 4597
Plumbers and gasfitters 4593
Employees of government (not clerks) 4275
Lawyers 4259
Bookbinders and finishers 4158
Musicians (professional) and teachers music 3980
Traders and dealers in liquors and wines 3944
Clerks and copyists 3763
Tinners and tinware makers 3555
Marble and stone cutters 3491
Iron and steel works and shops operatives 3463
Physicians and surgeons 3291
Coopers 3283
Hat and cap makers 3251
Apprentices to trades 3238
Employees of railroad companies 3188
Traders and dealers in dry foods, fancy foods, and notions 3135
Gold and silver workers and jewelers 3081
Box factory operatives 2903
Bankers and brokers 2900
Mill and factory operatives (not specified) 2776
Nurses 2567
Officials and employees of street companies 2548
Ship carpenters, caulkers, riggers, and smiths 2502
Confectioners 2228
Brewers and maltsters 2186
Pianoforte makers and tuners 2152
Hostlers 2150
Watchmen (private) and detectives 2081
Clergy 2075
Officials and employees of companies (not clerks) 2038
Artificial-flower makers 2018
Shirt, cuff, and collar makers 2003
Glass-works operatives 1905
Traders and dealers in cigars and tobacco 1893
Traders and dealers in real estate 1860
Upholsterers 1848
Builders and contractors (not specified) 1805
Brass founders and workers 1791
Carpet makers 1756
Clerks and bookkeepers in manufacturing estabs. 1702
Manufacturers 1688
Wood turners, carvers, and woodenware makers 1672
Plasterers 1595
Silk mill operatives 1577
Boarding- and lodging-house keepers 1565
Boatmen and watermen 1561
Clerks and bookkeepers in banks 1555
Others in manufacturing, mechanical, and mining industries 1511
Traders and dealers in drugs and medicines 1498
Carriage and wagon makers 1419
Janitors 1419
Officials and employees of telegraph companies 1404
Gardeners, nurserymen, and vine-growers 1388
Tobacco factory operatives 1364
Restaurant keepers 1242
Actors 1240
Clock and watchmakers and repairers 1236
Milkmen and milkwomen 1211
Agricultural laborers 1199
Artists and teachers of art 1177
Agents (not specified) 1173
Commercial travelers 1147
Employees in manufacturing estabs. (not specified) 1142
Engravers 1129
Steam boiler makers 1120
Messengers 1115
Traders and dealers in clothing and men’s furnishing goods 1110
Farmers and planters 1106
Hotel keepers 1103
Employees of insurance companies (clerks) 1079
Traders and dealers in junk 1066
Harness and saddle makers 1023
Sugar makers and refiners 1000
Journalists 963
Gun- and lock-smiths 934
Fur workers 930
Packers 896
Traders and dealers in iron, tin, and copperware 870
Paper mill operatives 862
Clerks and bookkeepers in railroad offices 845
Clerks in government offices 841
Brokers (commercial) 840
Cotton-mill operatives 840
Broom and brush makers 834
Lace makers 833
Sewing machine operators 818
Traders and dealers in boots and shoes 802
Leather curriers, dressers, finishers, and tanners 784
Soldiers, sailors, and Marines (Army Navy) 768
Traders and dealers in coal and wood 748
Officials of government 710
Photographers 690
Dentists 688
Traders and dealers in cabinet ware 677
Rope and cordage makers 652
Roofers and slaters 641
Traders and dealers in books stationary 639
Rag pickers 635
Mirror and picture frame makers 615
Fishermen and oystermen 607
Undertakers 599
Wheelwrights 597
Livery-stable keepers 595
Collectors and claim agents 594
Bleachers, dyers and scourers 590
Sail and awning makers 585
Clerks in hotels and restaurants 557
Umbrella and parasol makers 531
Traders and dealers in newspapers periodicals 528
Florists 523
Bottlers and mineral-water makers 518
Gilders 513
Gentleman 511
Weighers, gaugers, and measurers 495
Engineers (civil) 490
Paperhangers 481
Traders and dealers in ice 475
Tool and cutlery makers 457
Steamboat men and women 453
Pattern makers 447
Wire makers and workers 443
Clerks and bookkeepers in offices 439
Gas-works employees 436
Leather case and pocket-book makers 435
Saw and planing mill operatives 432
Architects 422
Traders and dealers in crockery, china, glass, and stoneware 416
Designers, draughtsmen, and inventors 415
Blind, door and sash makers 408
Galloon, gimp, and tassel makers 405
Stewards and stewardesses 390
Pilots 388
Bag makers 377
Traders and dealers in livestock 368
Basket makers 365
Officials and employees of trade and transportation companies (not specified) 357
Millers 356
Traders and dealers in paper and paper stock 354
Publishers of books, maps, and newspapers 346
Candle, soap, and tallow makers 342
Meat packers, curers, and picklers 332
Corset makers 329
Potters 329
Traders and dealers in oils, paints, and turpentine 312
Traders and dealers in hats, caps, and furs 299
Copper workers 295
Whitewashers 289
Traders and dealers in leather, hides, and skins 288
Traders and dealers in lumber 274
Button-factory operatives 264
Chemical-works employees 263
Chemists, assayers, and metallurgists 259
Sawyers 252
Midwives 244
Trunk, valise, and carpet-bag makers 222
Miners 219
Oil mill and refinery operatives 215
Auctioneers 209
Traders and dealers in gold and silverware and jewelry 207
Woolen mill operatives 192
Newspaper criers and carriers 190
Car makers 189
Boat makers 187
Hair cleaners, dressers, and workers 187
Sexton 187
Lead and zinc works operatives 181
Other non-occupational response 169
Authors, lecturers, and literary persons 161
Officials, industry not specified 156
Mechanics (not specified) 154
Showmen and employees of shows 154
Others in professional and services 147
Dairymen and dairywomen 143
Straw workers 142
Employees in warehouses 140
Traders and dealers in sewing machines 133
Flax dressers 131
Glove makers 129
Traders and dealers in cotton and wool 121
Rubber factory operatives 120
Brick and tile makers 119
Britannia and japanned ware makers 117
Fertilizer establishment operatives 115
Officials of banks 114
Bone and ivory workers 109
Shippers and freighters 106
Veterinary surgeons 105
Hosiery and knitting mill operatives 103
Organ makers 100
Lumbermen and raftsmen 98
Traders and dealers in music and musical instruments 97
File makers, cutters, and grinders 90
Meat and fruit preserving establishment employees 90
Stove, furnace, and grate makers 88
Stock-drovers 86
Clerks and bookkeepers in companies 83
Employees of banks (not clerks) 73
Officials and employees of telephone companies 73
Distillers and rectifiers 66
Quarrymen 59
Billiard- and bowling saloon keepers and employees 49
Print-works operatives 49
Officers of the Army and Navy 45
Toll-gate and bridge keepers 40
Railroad builders and contractors 39
Scale and rule makers 38
Nail makers 36
Bridge builders and contractors 33
Traders and dealers in marble, stone and slate 33
Screw makers 32
Officials of manufacturing and mining companies 26
Pump makers 26
Charcoal and lime burners 23
Wood choppers 23
Employees of charitable institutions 22
Officials of railroad companies 22
Sewing machine factory operatives 15
Stock-raisers 10
Cheese makers 7
Turpentine farmers and laborers 7
Canalmen 6
Officials of insurance companies 6
Starch makers 6
Thread makers 6
Oil well operatives and laborers 5
Farm and plantation overseers 4
Hunters, trappers, guides, and scouts 4
Stock-herders 4
Agricultural implement makers 3
Salt makers 2
Apiarists 1
Quartz and stamp-mill operatives 1
Shingle and lath makers 1
Stave, shook, and heading makers 1
Traders and dealers in agricultural implements 1
  • Domestic servants appear to be the most popular occupation, followed by Clerks in Stores and Sailors.


Graph of Top 10 Occupations
combined_1880 %>%
  filter(labforce == "Yes, in the labor force" & 
           label != "Blank") %>%
  count(label) %>%
  top_n(10, wt = n) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#bc5090") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 70000, 10000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of the Top 10 Most Popular Occupations in 1880") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of Bottom 10 Occupations
combined_1880 %>%
  filter(labforce == "Yes, in the labor force" & 
           label != "Blank") %>%
  count(label) %>%
  top_n(-10, wt = n) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#bc5090") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 4, 1),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of the Top 10 Least Popular Occupations in 1880") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of All Occupations by Count
combined_1880 %>%
  filter(labforce == "Yes, in the labor force" & 
           label != "Blank") %>%
  count(label) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#bc5090") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 70000, 10000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of All Occupations by Count in 1880") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of All Occupations by Type
combined_1880 %>%
  filter(labforce == "Yes, in the labor force") %>%
  count(label) %>%
  
  ggplot(aes(y = n, x = label, n)) +
  geom_col(fill = "#bc5090") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 70000, 10000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of All Occupations by Type in 1880") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


1910

Top Jobs
combined_1910 %>%
  filter(labor_force == "Yes, in the labor force" & 
           label != "Not yet classified") %>%
  count(label) %>%
  arrange(desc(n)) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "300px")
label n
Operative and kindred workers (n.e.c.) 194803
Managers, officials, and proprietors (n.e.c.) 103049
Salesmen and sales clerks (n.e.c.) 100610
Private household workers (n.e.c.) 96602
Laborers (n.e.c.) 79582
Clerical and kindred workers (n.e.c.) 51887
Tailors and tailoresses 49130
Dressmakers and seamstresses, except factory 38128
Bookkeepers 30863
Carpenters 30238
Stenographers, typists, and secretaries 28895
Waiters and waitresses 21470
Painters, construction and maintenance 20375
Teachers (n.e.c.) 19669
Machinists 17521
Truck and tractor drivers 16676
Barbers, beauticians, and manicurists 15111
Porters 15098
Longshoremen and stevedores 14315
Plumbers and pipe fitters 14153
Janitors and sextons 13982
Laundresses, private household 13798
Compositors and typesetters 13079
Deliverymen and routemen 12432
Bakers 11699
Laundry and dry cleaning operatives 10939
Cooks, except private household 10729
Foremen (n.e.c.) 10558
Taxicab drivers and chauffers 10399
Meat cutters, except slaughter and packing house 10232
Bartenders 10070
Brickmasons, stonemasons, and tile setters 9881
Musicians and music teachers 9881
Electricians 9559
Milliners 8926
Real estate agents and brokers 8586
Lawyers and judges 8350
Messengers and office boys 8234
Shipping and receiving clerks 8048
Teamsters 7474
Stationary engineers 7464
Hucksters and peddlers 7439
Service workers, except private household (n.e.c.) 7396
Practical nurses 6902
Policemen and detectives 6845
Guards, watchmen, and doorkeepers 6741
Physicians and surgeons 6708
Actors and actresses 6354
Blacksmiths 5407
Shoemakers and repairers, except factory 5107
Farm laborers, wage workers 4970
Housekeepers, private household 4919
Telephone operators 4850
Boarding and lodging house keepers 4581
Motormen, street, subway, and elevated railway 4367
Craftsmen and kindred workers (n.e.c.) 4204
Charwomen and cleaners 4134
Insurance agents and brokers 4114
Bookbinders 4046
Tinsmiths, coppersmiths, and sheet metal workers 3951
Cashiers 3918
Plasterers 3630
Elevator operators 3557
Stationary firemen 3533
Painters, except construction or maintenance 3501
Conductors, bus and street railway 3209
Accountants and auditors 3140
Artists and art teachers 2921
Nurses, professional 2918
Jewelers, watchmakers, goldsmiths, and silversmiths 2769
Mail carriers 2630
Officers, pilots, pursers and engineers, ship 2589
Nurses, student professional 2572
Pharmacists 2554
Sailors and deck hands 2485
Mechanics and repairmen (n.e.c.) 2177
Collectors, bill and account 2170
Cabinetmakers 2168
Clergymen 2079
Editors and reporters 2076
Weavers, textile 2076
Structural metal workers 2059
Conductors, railroad 1994
Dentists 1973
Stone cutters and stone carvers 1941
Firemen, fire protection 1894
Molders, metal 1804
Designers 1780
Upholsterers 1758
Telegraph operators 1679
Housekeepers and stewards, except private household 1636
Pressmen and plate printers, printing 1601
Furriers 1524
Draftsmen 1496
Photographers 1473
Bootblacks 1312
Members of the armed services 1282
Architects 1240
Mine operatives and laborers 1212
Engineers, civil 1107
Boilermakers 1092
Funeral directors and embalmers 1030
Filers, grinders, and polishers, metal 1026
Attendants, hospital and other institution 927
Gardeners, except farm and groundskeepers 922
Chemists 862
Buyers and department heads, store 789
Farmers (owners and tenants) 786
Photoengravers and lithographers 766
Inspectors (n.e.c.) 706
Brakemen, railroad 694
Paperhangers 677
Locomotive engineers 612
Ticket, station, and express agents 599
Librarians 550
Roofers and slaters 533
Mechanics and repairmen, automobile 513
Linemen and servicemen, telegraph, telephone, and power 504
Advertising agents and salesmen 494
Lumbermen, raftsmen, and woodchoppers 479
Tool makers, and die makers and setters 448
Oilers and greaser, except auto 432
Subject not specified 430
Religious workers 428
Apprentices, building trades (n.e.c.) 408
Pattern and model makers, except paper 405
Sawyers 399
Telegraph messengers 380
Apprentices, other specified trades 372
Farm laborers, unpaid family workers 368
Fishermen and oystermen 367
Spinners, textile 356
Apprentices, printing trades 343
Managers and superintendents, building 326
Midwives 320
Engineers, mechanical 315
Officials and administrators (n.e.c.), public administration 314
Garage laborers and car washers and greasers 311
Apprentices, metalworking trades (n.e.c.) 302
Authors 292
Locomotive firemen 271
Ushers, recreation and amusement 268
Bank tellers 260
Inspectors, public administration 244
Attendants, professional and personal service (n.e.c.) 238
Buyers and shippers, farm products 223
Excavating, grading, and road machinery operators 220
Switchmen, railroad 216
Engravers, except photoengravers 199
Newsboys 192
Opticians and lens grinders and polishers 181
Professional, technical and kindred workers (n.e.c.) 181
Motion picture projectionists 180
Piano and organ tuners and repairmen 174
Millwrights 146
Engineers, electrical 141
Apprentice electricians 131
Apprentice machinists and toolmakers 120
Engineers, mining 119
Boatmen, canalmen, and lock keepers 116
Demonstrators 116
Dyers 115
Apprentices, trade not specified 102
Bus drivers 102
Floormen and floor managers, store 96
Inspectors, scalers, and graders, log and lumber 93
Watchmen (crossing) and bridge tenders 92
Engineers (n.e.c.) 91
Electrotypers and stereotypers 87
Mechanics and repairmen, railroad and car shop 85
Recreation and group workers 77
Motormen, mine, factory, logging camp, etc. 72
Therapists and healers (n.e.c.) 71
Surveyors 67
Stock and bond salesmen 63
Apprentice carpenters 62
Veterinarians 59
Millers, grain, flour, feed, etc. 55
Athletes 53
Entertainers (n.e.c.) 53
Express messengers and railway mail clerks 53
Auctioneers 51
Attendants, recreation and amusement 46
Attendants, physician’s and dentist’s office 45
Farm managers 45
Sports instructors and officials 43
Decorators and window dressers 35
Apprentice bricklayers and masons 33
Baggagemen, transportation 32
Power station operators 30
Dancers and dancing teachers 26
College presidents and deans 25
Officials, lodge, society, union, etc. 22
Apprentice auto mechanics 21
Marshals and constables 19
Farm foremen 16
Agents (n.e.c.) 14
Mechanics and repairmen, office machine 12
Osteopaths 12
Counter and fountain workers 11
Optometrists 11
Postmasters 9
Cement and concrete finishers 6
Attendants and assistants, library 5
Loom fixers 5
Blasters and powdermen 4
Furnacemen, smeltermen and pourers 4
Sheriffs and bailiffs 4
Biological scientists 2
Heaters, metal 2
  • Domestic servants appear to be the most popular occupation, followed by Clerks in Stores and Sailors.


Graph of Top 10 Occupations
combined_1910 %>%
  filter(labor_force == "Yes, in the labor force" & 
           label != "Not yet classified") %>%
  count(label) %>%
  top_n(10, wt = n) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#003f5c") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 200000, 20000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of the Top 10 Most Popular Occupations in 1910") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of Bottom 10 Occupations
combined_1910 %>%
  filter(labor_force == "Yes, in the labor force" & 
           label != "Not yet classified") %>%
  count(label) %>%
  top_n(-10, wt = n) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#003f5c") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 11, 1),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of the Top 10 Least Popular Occupations in 1910") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of All Occupations by Count
combined_1910 %>%
  filter(labor_force == "Yes, in the labor force" & 
           label != "Not yet classified") %>%
  count(label) %>%
  
  ggplot(aes(y = n, x = reorder(label, n))) +
  geom_col(fill = "#003f5c") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 200000, 20000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of All Occupations by Count in 1910") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))


Graph of All Occupations by Type
combined_1910 %>%
  filter(labor_force == "Yes, in the labor force" & 
           label != "Not yet classified") %>%
  count(label) %>%
  
  ggplot(aes(y = n, x = label, n)) +
  geom_col(fill = "#003f5c") + 
  coord_flip() + 
  geom_text(aes(label = n),
            hjust = 0,
            family = "Quicksand") + 
  scale_y_continuous(breaks = seq(0, 200000, 20000),
                     expand = expand_scale(mult = c(0,0.1))) +
  
  labs(x = "", y = "Count",
       title = "Graph of All Occupations by Type in 1910") +
  theme(panel.grid.major.y = element_blank(),
        legend.text = element_text(colour = "black"))